We propose Residual Text Image Composer (RTIC) inspired by recent works on image-text composition method TIRG [1]}: emphasizing the encoding of the residual between the candidate and the target features that can be modularized with multiple blocks. Each block encodes the residual for the specific channels of the feature using a channel-wise attention score for a gating mechanism. Our experimental results support that the proposed method, RTIC, can marginally outperform the existing baseline, TIRG, using the same hyper-parameter setting for training. Moreover, we propose an iterative process of the ensemble that uses the previous best score matrix as the new candidate of the ensemble. We used hyperopthttps://github.com/hyperopt/hyperopt Bayesian optimization [2]} for the task, which aims to find optimal weights between similarity scores of the single models and maximize the score on the validation and the test. Finally, our approach achieves the 2nd place on the Fashion-IQ 2020 challenge with the ensemble of the discriminative models.
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